AI 中文总结
针对磁约束等离子体自动动力学分布拟合问题，提出基于修正双曲正切分布和优劣高斯混合似然的离群值稳健贝叶斯方法，解决相关障碍，经KSTAR实验验证，为大规模动力学分布生成提供基础。
AI 中文摘要
我们提出了一种离群值稳健的贝叶斯方法，用于对具有修正双曲正切（mtanh）参数化的磁约束等离子体进行自动动力学分布拟合，并在KSTAR上演示了其实现。该方法解决了两个系统性障碍：异常诊断通道会使最小二乘拟合产生偏差，以及mtanh代价曲面的多峰性会使确定性优化器陷入次要最小值。所采用的工作流程使用基于Box-Tiao公式的优劣高斯混合似然作为拟合诊断通道的默认离群值稳健似然，并将后验离群值概率保留为通道级质量指标。后验通过仿射不变系综MCMC采样器进行采样，该采样器在确定性最大后验（MAP）寻优结果附近初始化，降低了对多峰mtanh曲面上次要最小值的敏感性。一个批处理自动化层从MDSplus检索诊断数据，并对相关诊断可用的\(n_e\)、\(T_e\)、\(T_i\)和\(v_T\)的任意时间切片进行并行拟合。结果以适合MDSplus上传和下游分析的格式写入。代表性的KSTAR H模式案例表明，混合似然在保留合理的台座分布的同时，降低了受污染测量的权重。该工作流程为未来用于动力学-EFIT、TRANSP、FASTRAN和数据驱动分析工作流程的大规模动力学分布生成提供了实际基础。
英文摘要
We present an outlier-robust Bayesian approach for automated kinetic profile fitting in magnetically confined plasmas with the modified tanh (mtanh) parametrisation and demonstrate its implementation on KSTAR. The method addresses two systematic obstacles: anomalous diagnostic channels can bias least-squares fits, and multimodality of the mtanh cost surface can trap deterministic optimisers in secondary minima. The deployed workflow uses a good-and-bad Gaussian mixture likelihood based on the Box--Tiao formulation as the default outlier-robust likelihood for fitted diagnostic channels, with posterior outlier probabilities retained as channel-level quality indicators. The posterior is sampled with an affine-invariant ensemble MCMC sampler initialised near the result of deterministic maximum a posteriori (MAP)-seeking optimisation, reducing sensitivity to secondary minima on the multimodal mtanh surface. A batch automation layer retrieves diagnostic data from MDSplus and fits arbitrary time slices in parallel for the quantities \(n_e\), \(T_e\), \(T_i\), and \(v_T\) for which the relevant diagnostics are available. Results are written in formats suitable for MDSplus upload and downstream analysis. Representative KSTAR H-mode cases show that the mixture likelihood downweights contaminated measurements while preserving plausible pedestal profiles. The workflow provides a practical basis for future large-scale kinetic profile production for kinetic-EFIT, TRANSP, FASTRAN, and data-driven analysis workflows.
Comments16 pages, 8 figures; submitted to Nuclear Fusion
